Untargeted Studies Research Articles

Generating pooled quality control samples of volatile organic compounds

Untargeted analysis of volatile organic compounds (VOCs) from exhaled breath and culture headspace are influenced by several confounding factors not represented in reference standards. In this study, we propose a method of generating pooled quality control (QC) samples for untargeted VOC studies using a split-recollection workflow with thermal desorption tubes. Sample tubes were desorbed and split from each sample and recollected onto a single tube, generating a pooled QC sample. This QC sample was then repeatedly desorbed and recollected with a sequentially lower split ratio allowing injection of multiple QC samples. We found pooled QC samples to be representative of complex mixtures using principal component analysis and may be useful in future longitudinal, multi-centre, and validation studies to assess data quality and adjust for batch effects.

An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.

Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power (Rp > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies. In this manuscript, a proof-of-concept study using ATP10D dysfunctional murine models was investigated to demonstrate the utility of HRdm-incorporated untargeted lipidomic analysis pipelines. Total lipid features were found to increase by 2.5-fold with HRdm compared to demultiplexed DTIMS as a consequence of more isomeric lipids being resolved. An example lipid, PC 36:5, was found to be significantly higher in dysfunctional ATP10D mice with two resolved peaks observed by HRdm that were absent in both the functional ATP10D mice and the standard demultiplexed DTIMS acquisition mode. The benefits of utilizing HRdm for discerning isomeric lipids in untargeted workflows have the potential to enhance our analytical understanding of lipids related to disease complexity and biologically relevant studies.

Impact of Partial Body Shielding from Very High Dose Rates on Untargeted Metabolomics in Biodosimetry.

A realistic exposure to ionizing radiation (IR) from an improvised nuclear device will likely include individuals who are partially shielded from the initial blast delivered at a very high dose rate (VHDR). As different tissues have varying levels of radiosensitivity, e.g., hematopoietic vs gastrointestinal tissues, the effects of shielding on radiation biomarkers need to be addressed. Here, we explore how biofluid (urine and serum) metabolite signatures from male and female C57BL/6 mice exposed to VHDR (5-10 Gy/s) total body irradiation (TBI, 0, 4, and 8 Gy) compare to individuals exposed to partial body irradiation (PBI) (lower body irradiated [LBI] or upper body irradiated [UBI] at an 8 Gy dose) using a data-independent acquisition untargeted metabolomics approach. Although sex differences were observed in the spatial groupings of urine signatures from TBI and PBI mice, a metabolite signature (N6,N6,N6-trimethyllysine, carnitine, propionylcarnitine, hexosamine-valine-isoleucine, taurine, and creatine) previously developed from variable dose rate experiments was able to identify individuals with high sensitivity and specificity, irrespective of radiation shielding. A panel of serum metabolites composed from previous untargeted studies on nonhuman primates had excellent performance for separating irradiated cohorts; however, a multiomic approach to complement the metabolome could increase dose estimation confidence intervals. Overall, these results support the inclusion of small-molecule markers in biodosimetry assays without substantial interference from the upper or lower body shielding.

FragHub: A Mass Spectral Library Data Integration Workflow.

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

Cerebrospinal fluid shotgun proteomics identifies distinct proteomic patterns in cerebral amyloid angiopathy rodent models and human patients

Cerebral amyloid angiopathy (CAA) is a form of small vessel disease characterised by the progressive deposition of amyloid β protein in the cerebral vasculature, inducing symptoms including cognitive impairment and cerebral haemorrhages. Due to their accessibility and homogeneous disease phenotypes, animal models are advantageous platforms to study diseases like CAA. Untargeted proteomics studies of CAA rat models (e.g. rTg-DI) and CAA patients provide opportunities for the identification of novel biomarkers of CAA. We performed untargeted, data-independent acquisition proteomic shotgun analyses on the cerebrospinal fluid of rTg-DI rats and wild-type (WT) littermates. Rodents were analysed at 3 months (n = 6/10), 6 months (n = 8/8), and 12 months (n = 10/10) for rTg-DI and WT respectively. For humans, proteomic analyses were performed on CSF of sporadic CAA patients (sCAA) and control participants (n = 39/28). We show recurring patterns of differentially expressed (mostly increased) proteins in the rTg-DI rats compared to wild type rats, especially of proteases of the cathepsin protein family (CTSB, CTSD, CTSS), and their main inhibitor (CST3). In sCAA patients, decreased levels of synaptic proteins (e.g. including VGF, NPTX1, NRXN2) and several members of the granin family (SCG1, SCG2, SCG3, SCG5) compared to controls were discovered. Additionally, several serine protease inhibitors of the SERPIN protein family (including SERPINA3, SERPINC1 and SERPING1) were differentially expressed compared to controls. Fifteen proteins were significantly altered in both rTg-DI rats and sCAA patients, including (amongst others) SCG5 and SERPING1. These results identify specific groups of proteins likely involved in, or affected by, pathophysiological processes involved in CAA pathology such as protease and synapse function of rTg-DI rat models and sCAA patients, and may serve as candidate biomarkers for sCAA.

LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation

Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.

Determination of volatile compounds in different parts of grass carp using GC✕GC-MS combined with chemometrics

The flavor of freshwater fish, which is a highly complex mixture of volatile molecules in varying concentrations, has a nasty and earthy odor. Many compounds of freshwater fish cannot be differentiated due to insufficient separation in traditional one-dimensional gas chromatography-mass spectrometry (GC-MS). This study employed an improved method to detect volatile compounds in different parts of grass carp, including the dorsal meat, skin, intestines, belly, fins, and gills. Comprehensive two-dimensional gas chromatography-mass spectrometry (GC✕GC-MS) with high resolution power was utilized to fully separate the volatile compounds. Additionally, an in-house written script based on Matlab and chemometrics was used to conduct large scale and untargeted studies on volatile compounds. The results demonstrated the superior capability of GC✕GC-MS in identifying a greater number of volatile compounds. The in-house written script was used to automatically compare the same compounds from different samples. Through OPLS-DA analysis, 51 key volatile compound candidates, including dihydrojasmone, cryptomeridiol, (Z,Z)-3,6-nonadienal, 3,5-octadien-2-one, and others, were identified as the most-influential variables in differentiating different parts of grass carp. PCA analysis revealed that the 51 key volatile compound candidates still retained information about the origin of different parts, despite their low concentration. Furthermore, expect that most of these compounds have been found in fish or fish-related products, some may originate from plants, insecticides, or pollution.

Investigating the Metabolism of Plants Germinated in Heavy Water, D2O, and H218O-Enriched Media Using High-Resolution Mass Spectrometry.

Mass spectrometry has been an essential technique for the investigation of the metabolic pathways of living organisms since its appearance at the beginning of the 20th century. Due to its capability to resolve isotopically labeled species, it can be applied together with stable isotope tracers to reveal the transformation of particular biologically relevant molecules. However, low-resolution techniques, which were used for decades, had limited capabilities for untargeted metabolomics, especially when a large number of compounds are labelled simultaneously. Such untargeted studies may provide new information about metabolism and can be performed with high-resolution mass spectrometry. Here, we demonstrate the capabilities of high-resolution mass spectrometry to obtain insights on the metabolism of a model plant, Lepidium sativum, germinated in D2O and H218O-enriched media. In particular, we demonstrated that in vivo labeling with heavy water helps to identify if a compound is being synthesized at a particular stage of germination or if it originates from seed content, and tandem mass spectrometry allows us to highlight the substructures with incorporated isotope labels. Additionally, we found in vivo labeling useful to distinguish between isomeric compounds with identical fragmentation patterns due to the differences in their formation rates that can be compared by the extent of heavy atom incorporation.

Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies.

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.

Multi-omics characterization of NIST seafood reference materials and alternative matrix preparations.

The National Institute of Standards and Technology (NIST) has prepared four seafood reference materials (RMs) for use in food safety and nutrition studies: wild-caught and aquacultured salmon (RM 8256 and RM 8257) and wild-caught and aquacultured shrimp (RM 8258 and RM 8259). These materials were characterized using genetic, metabolomic (1H-NMR, nuclear magnetic resonance and LC-HRMS/MS, liquid chromatography high-resolution tandem mass spectrometry), lipidomic, and proteomic methods to explore their use as matrix-matched, multi-omic differential materials for method development towards identifying product source and/or as quality control in untargeted omics studies. The results from experimental replicates were reproducible for each reference material and analytical method, with the most abundant features reported. Additionally, differences between the materials could be detected, where wild-caught and aquacultured seafood could be distinguished using untargeted metabolite, lipid, and protein analyses. Further processing of the fresh-frozen RMs by freeze-drying revealed the freeze-dried seafoods could still be reliably discerned. These results demonstrate the usefulness of these reference materials as tools for omics instrument validation and measurement harmonization in seafood-related studies. Furthermore, their use as differential quality control (QC) materials, regardless of preparation method, may also provide a tool for laboratories to demonstrate proficiency at discriminating between products based on source/species.

Metabolomic Reconfiguration in Primed Barley (Hordeum vulgare) Plants in Response to Pyrenophora teres f. teres Infection.

Necrotrophic fungi affect a wide range of plants and cause significant crop losses. For the activation of multi-layered innate immune defences, plants can be primed or pre-conditioned to rapidly and more efficiently counteract this pathogen. Untargeted and targeted metabolomics analyses were applied to elucidate the biochemical processes involved in the response of 3,5-dichloroanthranilic acid (3,5-DCAA) primed barley plants to Pyrenophora teres f. teres (Ptt). A susceptible barley cultivar ('Hessekwa') at the third leaf growth stage was treated with 3,5-DCAA 24 h prior to infection using a Ptt conidia suspension. The infection was monitored over 2, 4, and 6 days post-inoculation. For untargeted studies, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) was used to analyse methanolic plant extracts. Acquired data were processed to generate the data matrices utilised in chemometric modelling and multi-dimensional data mining. For targeted studies, selected metabolites from the amino acids, phenolic acids, and alkaloids classes were quantified using multiple reaction monitoring (MRM) mass spectrometry. 3,5-DCAA was effective as a priming agent in delaying the onset and intensity of symptoms but could not prevent the progression of the disease. Unsupervised learning methods revealed clear differences between the sample extracts from the control plants and the infected plants. Both orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and 'shared and unique structures' (SUS) plots allowed for the extraction of potential markers of the primed and naïve plant responses to Ptt. These include classes of organic acids, fatty acids, amino acids, phenolic acids, and derivatives and flavonoids. Among these, 5-oxo-proline and citric acid were notable as priming response-related metabolites. Metabolites from the tricarboxylic acid pathway were only discriminant in the primed plant infected with Ptt. Furthermore, the quantification of targeted metabolites revealed that hydroxycinnamic acids were significantly more prominent in the primed infected plants, especially at 2 d.p.i. Our research advances efforts to better understand regulated and reprogrammed metabolic responses that constitute defence priming in barley against Ptt.

Open tubular liquid chromatography: Recent advances and future trends.

Nano-liquid chromatography (nanoLC) is gaining significant attention as a primary analytical technique across various scientific domains. Unlike conventional high-performance LC, nanoLC utilizes columns with inner diameters (i.ds.) usually ranging from 10 to 150μm and operates at mobile phase flow rates between 10 and 1000 nl/min, offering improved chromatographic performance and detectability. Currently, most exploration of nanoLC has focused on particle-packed columns. Although open tubular LC (OTLC) can provide superior performance, optimized OTLC columns require very narrow i.ds.(<10μm) and demand challenging instrumentation. At the moment, these challenges have limited the success of OTLC. Nevertheless, remarkable progress has been made in developing and utilizing OTLC systems featuring narrow columns(<2μm). Additionally, significant efforts have been made to explore larger columns (10-75μm i.d), demonstrating practical applicability in many situations. Due to their perceived advantages, interest in OTLC has resurged in the last two decades. This review provides an updated outlook on the latest developments in OTLC, focusing on instrumental challenges, achievements, and advancements in column technology. Moreover, it outlines selected applications that illustrate the potential of OTLC for performing targeted and untargeted studies.

Quantitative multiorgan proteomics of fatal COVID‐19 uncovers tissue‐specific effects beyond inflammation

SARS‐CoV‐2 may directly and indirectly damage lung tissue and other host organs, but there are few system‐wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus‐infected FFPE tissues. The first layer of response to SARS‐CoV‐2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue‐specific effects to reflect distinct COVID‐19‐associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non‐COVID‐19 patients. Extensive organ‐specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS‐proteomics‐derived results contribute substantially to our understanding of COVID‐19 pathomechanisms and suggest strategies for organ‐specific therapeutic interventions.

Роль биомаркеров мочи в диагностике туберкулеза легких (обзор)

Diagnosis of tuberculosis (TB) based on sputum analysis has limitations for certain categories of patients (older adults, children, and people living with HIV). An alternative approach is a non-invasive urine-based method, which provides a large sample volume and a quick result. We searched for English-language publications from 2010 to 2021 in PubMed and Cochrane databases using the terms tuberculosis + urine + biomarkers. Papers on urine lipoarabinomannan testing were excluded as this topic has been sufficiently studied elsewhere. The reviewed publications cover more than 30 urine biomarkers used to diagnose TB and evaluate treatment effectiveness. Urine transrenal DNA extraction continues to be investigated, although its diagnostic sensitivity and specificity depend on the extraction method and patient’s HIV status. IP-10 is likely to be a non-specific inflammatory marker; however, its level correlates with TB/HIV status and may be useful for assessing TB treatment response. Further, the article shows the potential of metabolomic biomarkers and biosignatures for measuring the activity of TB infection and distinguishing it from other respiratory diseases. The number of reliable biomarkers predicting TB treatment outcomes is limited. Numerous untargeted studies have used mass spectrometry to detect metabolomic and proteomic biomarkers in urine. The publications are heterogeneous in design and methods; few studies have analysed the specificity and sensitivity of the diagnostic methods covered. In future, a combination of host and pathogen biomarkers could increase the sensitivity and specificity of TB diagnosis.

Abstract P204: Associations Between Dairy Intake and Serum Metabolites in Middle-Aged Adults in the U.S.

Introduction: There is a lack of objective biomarkers of dietary intake. Untargeted studies can unbiasedly identify dairy biomarkers and inform the creation of robust dietary assessment tools that integrate questionnaire and biospecimen data. Methods: Dairy products from interviewer-administered questionnaires were categorized as either low-fat or high-fat dairy. Total dairy was a composite of low-fat and high-fat dairy items. Linear regression models were used to assess associations between dairy intake and 360 serum metabolites in the Atherosclerosis Risk in Communities (ARIC) Study (n=3907). Significant associations were then tested for replication in the Bogalusa Heart Study (BHS) (n=819). Results: In the ARIC study, 34 dairy-metabolite associations were identified (total dairy, n = 16; low-fat dairy, n = 10; high-fat dairy, n = 8). Total dairy shared 7 metabolic associations each with low-fat dairy and high-fat dairy. Low-fat dairy was associated with pantothenate, vitamin B5, and the catabolic product of vitamin B6, pyridoxate. High-fat dairy was associated with one amino acid, 3-hydroxy-2-ethylpropionate, and eight lipid metabolites, including myristate (14:0), 10-nonadecenoate (19:1n9), and myristoleate (14:1n5). Of the metabolites available for replication in BHS (total dairy, n = 15; low-fat dairy, n = 9; high-fat dairy, n = 8), 2 total dairy metabolites were replicated and 2 low-fat dairy metabolites were replicated ( Table ). Conclusions: We identified metabolomic markers of total, low-fat, and high-fat dairy. Pantothenate, myristate (14:0), and tiglyl carnitine replicated across two large, diverse study populations, suggesting that they are markers of dairy consumption.

UPLC-Q-TOF-MS-based untargeted studies of the secondary metabolites secreted by Sclerotinia sclerotiorum under the axenic condition

The stem rot disease has emerged globally as a major threat to oilseed Brassica's productivity and seed quality. The generalist causal pathogen Sclerotinia sclerotiorum (Lib.) de Bary shows large variability in their aggressiveness and pathogenicity. Revealing the pathogen's metabolic profile and signaling components in host-pathogen interaction is fundamental in understanding host resistance to the disease. In this study, the metabolites released by the pathogenic strains of S. sclerotiorum under the axenic culture have been identified using the untargeted high-resolution UPLC-QTOF-ESI-MS/MS. The analysis of the ethyl acetate extracts of the S. sclerotiorum culture revealed ten major secondary metabolites namely, sclerin, sclerotinin-B, sclerone, melanin, bostrycoidin, botcinin-D, botcinin-A, gliovirin, scleramide, and botcinic acid. The later six metabolites are being reported for the first time in the culture extract of the S. sclerotiorum pathogen. Based on the overlapping and unique informative peaks in the chromatograms, the six S. sclerotiorum strains were grouped into three major clades in the phylogenetic analysis. The clustering based on metabolic profiles does not substantiate the diversity based on morphology or virulence differences over the host. The findings of the study signified the metabolites secreted under the axenic conditions are varies based on their growth and developmental stages and may not necessarily be the determining factors for their differential aggressiveness and virulence to their host.

Acylcarnitines in Ophthalmology: Promising Emerging Biomarkers.

Several common ocular diseases are leading causes of irreversible visual impairment. Over the last decade, various mainly untargeted metabolic studies have been performed to show that metabolic dysfunction plays an important role in the pathogenesis of ocular diseases. A number of metabolites in plasma/serum, aqueous or vitreous humor, or in tears have been found to differ between patients and controls; among them are L-carnitine and acylcarnitines, which are essential for mitochondrial fatty acid oxidation. The metabolic profile of carnitines regarding a variety of diseases has attracted researchers' interest. In this review, we present and discuss recent advances that have been made in the identification of carnitines as potential metabolic biomarkers in common ocular diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, central retinal vein occlusion, primary open-angle glaucoma, rhegmatogenous retinal detachment, and dry eye syndrome.

Overexpression of bacteriophage T4 and T7 endolysins differentially regulate the metabolic fingerprint of host Escherichia coli

Bioactive proteins are often overexpressed in different host systems for biotechnological/biomedical applications. Endolysins are natural bactericidal proteins that cleave the bacterial peptidoglycan membrane, and have the potential to be the next-generation enzybiotics. Therefore, the present study aims to elucidate the impact of two endolysins (T4L, T7L) overexpression on metabolic fingerprint of E. coli using NMR spectroscopy. The 1H NMR-based metabolomics analysis revealed global metabolite profiles of E. coli in response to endolysins. The study has identified nearly 75 metabolites, including organic acids, amino acids, sugars and nucleic acids. RNA Polymerase (RNAP) has been considered as reference protein for marking the specific alterations in metabolic pathways. The data suggested downregulation of central carbon metabolic pathway in both endolysins overexpression, but to a different extent. Also, the endolysin overexpression have highlighted the enhanced metabolic load and stress generation in the host cells, thus leading to the activation of osmoregulatory pathways. The overall changes in metabolic fingerprint of E. coli highlights the enhanced perturbations during the overexpression of T4L as compared to T7L. These untargeted metabolic studies shed light on the regulation of molecular pathways during the heterologous overexpression of these lytic enzymes that are lethal to the host.

Lipidomics profiling of zebrafish liver through untargeted liquid chromatography-high resolution mass spectrometry.

Lipidomics analysis of zebrafish tissues has shown promising results to understand disease-related outcomes of exposure to toxic substances at a molecular level. However, knowledge about their lipidome is limited, as most untargeted studies only identify the lipids that are statistically significant in their setup. In this work, liquid chromatography-high resolution mass spectrometry was used to study different aspects of the analytical workflow, that is, extraction solvents (methanol/chloroform/water (3/2/2, v/v/v), methanol/dichloromethane/water (2/3/2, v/v/v) and methanol/methyl-tert-butyl ether/water (3/10/2.5, v/v/v), instrumental response, and strategies used for lipid annotation. The number of high-quality features (relative standard deviation of the intensity values≤10% in the range 103 -107 counts) was affected by the dilution of lipid extracts, indicating that it is an important parameter for developing untargeted methods. The workflows used allowed the selection of a dilution factor to annotate 712 lipid species (507 bulk lipids) in zebrafish liver using four software (LipidMatch, LipidHunter, MS-DIAL, and Lipostar). Retention time mapping was a valuable tool to filter lipid annotations obtained from automatic software annotations. The lipid profiling of zebrafish livers will help in a better understanding of the true constitution of their lipidome at the species level, as well as in the use of zebrafish in toxicological studies.

Abstract 6339: Deep plasma proteomics at scale enabling proteogenomic analyses in a lung cancer (NSCLC) study

Abstract Our ~20,000 genes encode over one million protein variants, given alternative splice forms, allelic variation, and protein modification. Though large-scale genomics studies have expanded our understanding of biology, similarly, scaled deep and untargeted proteomics studies of biofluids have remained impractical due to complex workflows. To address this need, we have previously described Proteograph࣪, a novel platform that leverages the protein-corona interactions of nanoparticles for deep and untargeted proteomic sampling at scale. Using Proteograph࣪ in a non-small cell lung cancer (NSCLC) cohort, we previously conducted a deep interrogation of plasma from 141 subjects: 61 early-stage NSCLC subjects and 80 non-cancer controls. We identified 2,499 plasma proteins, with 1,992 present in ≥ 25% of the samples. Leveraging this data, we created a biomarker classifier distinguishing NSCLC from controls with area under the receiver operating characteristic curve of 0.911. In this study, we now re-analyze the data with the more sensitive DIA-NN software to enhance protein depth while preserving the accuracy of the classifier. In addition, to show the added value of our platform in combination with genomic data, we integrate previously sequenced exome data with our proteomic data to build a multi-modal proteogenomic deep learning classifier. Our results outline proteogenomic workflows for robust biomarker discovery and cohort subtyping. The Proteograph࣪ platform interrogates the plasma proteome at previously impractical combinations of scale, depth and coverage, and enables the development of improved classification models and the study of proteogenomics. 1Blume et al. Nat. Comm. (2020) Citation Format: Mahdi Zamanighomi, Harendra Guturu, Jian Wang, Amir Alavi, Tristan Brown, Daniel Hornburg, Moaraj Hasan, Shadi Ferdosi, Khatereh Motamedchaboki, Margaret Donovan, Theodore Platt, Ryan Benz, Asim Siddiqui, Serafim Batzoglou. Deep plasma proteomics at scale enabling proteogenomic analyses in a lung cancer (NSCLC) study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6339.